A 39 kDa fragment of endogenous ASK1 suggests specific cleavage not degradation by the proteasome

Transfected human apoptosis signal-regulating kinase 1 (ASK1) produces a 150 kDa protein. However, we have detected endogenous ASK1 predominantly as 39 and 50 kDa C-terminal and 75 and 110 kDa N-terminal fragments in a panel of nontransfected cancer cell lines and HUVEC endothelial cells. This suggests that in nonapoptotic cells, endogenous ASK1 protein is normally cleaved at a number of specific sites, some of which are in the kinase domain. Transfected ASK1 protein is known to be degraded by the proteasome. In contrast, the cleavage of endogenous ASK1 is independent of the proteasome as treatment with the proteasome inhibitor, lactacystin did not inhibit cleavage. Cisplatin treatment decreased the amount of 39 kDa C-terminal ASK1 fragments in mutant p53 cell lines suggesting a decrease in cleavage associated with apoptosis. Transfected ASK1 may, therefore, not accurately reflect the role of endogenous ASK1

Non-invasive and non-destructive measurements of confluence in cultured adherent cell lines

Many protocols used for measuring the growth of adherent monolayer cells in vitro are invasive, destructive and do not allow for the continued, undisturbed growth of cells within flasks. Protocols often use indirect methods for measuring proliferation. Microscopy techniques can analyse cell proliferation in a non-invasive or non-destructive manner but often use expensive equipment and software algorithms. In this method images of cells within flasks are captured by photographing under a standard inverted phase contract light microscope using a digital camera with a camera lens adaptor. Images are analysed for confluence using ImageJ freeware resulting in a measure of confluence known as an Area Fraction (AF) output. An example of the AF method in use on OVCAR8 and UPN251 cell lines is included. •Measurements of confluence from growing adherent cell lines in cell culture flasks is obtained in a non-invasive, non-destructive, label-free manner. •The technique is quick, affordable and eliminates sample manipulation. •The technique provides an objective, consistent measure of when cells reach confluence and is highly correlated to manual counting with a haemocytometer. The average correlation co-efficient from a Spearman correlation (n = 3) was 0.99 ± 0.008 for OVCAR8 (p = 0.01) and 0.99 ± 0.01 for UPN251 (p = 0.01) cell lines

In vitro development of chemotherapy and targeted therapy drug-resistant cancer cell lines: a practical guide with case studies

The development of a drug-resistant cell line can take from 3 to 18 months. However, little is published on the methodology of this development process. This article will discuss key decisions to be made prior to starting resistant cell line development; the choice of parent cell line, dose of selecting agent, treatment interval, and optimizing the dose of drug for the parent cell line. Clinically relevant drug-resistant cell lines are developed by mimicking the conditions cancer patients experience during chemotherapy and cell lines display between two- and eight-fold resistance compared to their parental cell line. Doses of drug administered are low, and a pulsed treatment strategy is often used where the cells recover in drug-free media. High-level laboratory models are developed with the aim of understanding potential mechanisms of resistance to chemotherapy agents. Doses of drug are higher and escalated over time. It is common to have difficulty developing stable clinically relevant drug-resistant cell lines. A comparative selection strategy of multiple cell lines or multiple chemotherapeutic agents mitigates this risk and gives insight into which agents or type of cell line develops resistance easily. Successful selection strategies from our research are presented. Pulsed-selection produced platinum or taxane-resistant large cell lung cancer (H1299 and H460) and temozolomide-resistant melanoma (Malme-3M and HT144) cell lines. Continuous selection produced a lapatinib-resistant breast cancer cell line (HCC1954). Techniques for maintaining drug-resistant cell lines are outlined including; maintaining cells with chemotherapy, pulse treating with chemotherapy, or returning to master drug-resistant stocks. The heterogeneity of drug-resistant models produced from the same parent cell line with the same chemotherapy agent is explored with reference to P-glycoprotein. Heterogeneity in drug-resistant cell lines reflects the heterogeneity that can occur in clinical drug resistance

The EMT-activator ZEB1 is unrelated to platinum drug resistance in ovarian cancer but is predictive of survival

The IGROVCDDP cisplatin-resistant ovarian cancer cell line is an unusual model, as it is also cross-resistant to paclitaxel. IGROVCDDP, therefore, models the resistance phenotype of serous ovarian cancer patients who have failed frontline platinum/taxane chemotherapy. IGROVCDDP has also undergone epithelial-mesenchymal transition (EMT). We aim to determine if alterations in EMT-related genes are related to or independent from the drug-resistance phenotypes. EMT gene and protein markers, invasion, motility and morphology were investigated in IGROVCDDP and its parent drug-sensitive cell line IGROV-1. ZEB1 was investigated by qPCR, Western blotting and siRNA knockdown. ZEB1 was also investigated in publicly available ovarian cancer gene-expression datasets. IGROVCDDP cells have decreased protein levels of epithelial marker E-cadherin (6.18-fold, p = 1.58e−04) and higher levels of mesenchymal markers vimentin (2.47-fold, p = 4.43e−03), N-cadherin (4.35-fold, p = 4.76e−03) and ZEB1 (3.43-fold, p = 0.04). IGROVCDDP have a spindle-like morphology consistent with EMT. Knockdown of ZEB1 in IGROVCDDP does not lead to cisplatin sensitivity but shows a reversal of EMT-gene signalling and an increase in cell circularity. High ZEB1 gene expression (HR = 1.31, n = 2051, p = 1.31e−05) is a marker of poor overall survival in high-grade serous ovarian-cancer patients. In contrast, ZEB1 is not predictive of overall survival in high-grade serous ovarian-cancer patients known to be treated with platinum chemotherapy. The increased expression of ZEB1 in IGROVCDDP appears to be independent of the drug-resistance phenotypes. ZEB1 has the potential to be used as biomarker of overall prognosis in ovarian-cancer patients but not of platinum/taxane chemoresistance

OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets

Background: Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade. METHODS: We have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples. RESULTS: To demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10-6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12. CONCLUSIONS/IMPACT: OvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://glados.ucd.ie/OvMark/index.html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities

Identifying novel hypoxia-associated markers of chemoresistance in ovarian cancer

BACKGROUND Ovarian cancer is associated with poor long-term survival due to late diagnosis and development of chemoresistance. Tumour hypoxia is associated with many features of tumour aggressiveness including increased cellular proliferation, inhibition of apoptosis, increased invasion and metastasis, and chemoresistance, mostly mediated through hypoxia-inducible factor (HIF)-1α. While HIF-1α has been associated with platinum resistance in a variety of cancers, including ovarian, relatively little is known about the importance of the duration of hypoxia. Similarly, the gene pathways activated in ovarian cancer which cause chemoresistance as a result of hypoxia are poorly understood. This study aimed to firstly investigate the effect of hypoxia duration on resistance to cisplatin in an ovarian cancer chemoresistance cell line model and to identify genes whose expression was associated with hypoxia-induced chemoresistance. METHODS Cisplatin-sensitive (A2780) and cisplatin-resistant (A2780cis) ovarian cancer cell lines were exposed to various combinations of hypoxia and/or chemotherapeutic drugs as part of a 'hypoxia matrix' designed to cover clinically relevant scenarios in terms of tumour hypoxia. Response to cisplatin was measured by the MTT assay. RNA was extracted from cells treated as part of the hypoxia matrix and interrogated on Affymetrix Human Gene ST 1.0 arrays. Differential gene expression analysis was performed for cells exposed to hypoxia and/or cisplatin. From this, four potential markers of chemoresistance were selected for evaluation in a cohort of ovarian tumour samples by RT-PCR. RESULTS Hypoxia increased resistance to cisplatin in A2780 and A2780cis cells. A plethora of genes were differentially expressed in cells exposed to hypoxia and cisplatin which could be associated with chemoresistance. In ovarian tumour samples, we found trends for upregulation of ANGPTL4 in partial responders and down-regulation in non-responders compared with responders to chemotherapy; down-regulation of HER3 in partial and non-responders compared to responders; and down-regulation of HIF-1α in non-responders compared with responders. CONCLUSION This study has further characterized the relationship between hypoxia and chemoresistance in an ovarian cancer model. We have also identified many potential biomarkers of hypoxia and platinum resistance and provided an initial validation of a subset of these markers in ovarian cancer tissues